A receiver, also known as User Equipment (UE), mobile station, wireless terminal and/or mobile terminal is enabled to communicate wirelessly in a wireless communication network, sometimes also referred to as a cellular radio system. The communication may be made, e.g., between two receivers, between a receiver and a wire connected telephone and/or between a receiver and a server via a Radio Access Network (RAN) and possibly one or more core networks.
The wireless communication may comprise various communication services such as voice, messaging, packet data, video, broadcast, etc.
The receivers may further be referred to as mobile telephones, cellular telephones, computer tablets or laptops with wireless capability. The receivers in the present context may be, for example, portable, pocket-storable, hand-held, computer-comprised, or vehicle-mounted mobile devices, enabled to communicate voice and/or data, via the radio access network, with another entity, such as another receiver or a server.
The wireless communication network covers a geographical area which is divided into cell areas, with each cell area being served by a radio network node, or base station, e.g., a Radio Base Station (RBS), which in some networks may be referred to as transmitter, eNodeB (eNB), NodeB, or B node, depending on the technology and terminology used. The network nodes may be of different classes, e.g., macro eNodeB, home eNodeB or pico base station, based on transmission power and thereby also cell size.
Sometimes, the expression “cell” may be used for denoting the radio network node itself. However, the cell may also in normal terminology be used for the geographical area where radio coverage is provided by the radio network node/base station at a base station site. One radio network node, situated on the base station site, may serve one or several cells. The radio network nodes communicate over the air interface operating on radio frequencies with the receivers within range of the respective radio network node.
In some radio access networks, several radio network nodes may be connected, e.g., by landlines or microwave, to a Radio Network Controller (RNC), e.g., in Universal Mobile Telecommunications System (UMTS). The RNC, also sometimes termed Base Station Controller (BSC), e.g., in Global System for Mobile Communications (GSM), may supervise and coordinate various activities of the plural radio network nodes connected thereto.
In 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE), radio network nodes, which may be referred to as eNodeBs or eNBs, may be connected to a gateway, e.g., a radio access gateway, to one or more core networks.
In the present context, the expressions downlink, downstream link or forward link may be used for the transmission path from the radio network node to the receiver. The expression uplink, upstream link or reverse link may be used for the transmission path in the opposite direction, i.e., from the receiver to the radio network node.
Systems beyond 3G mobile communication, e.g., 3GPP LTE, offer high data rate in the downlink by employing Multiple-Input and Multiple-Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) access scheme at the UE receiver. LTE, e.g., UE category 5, downlink can support up to 300 Megabits per second (Mbps) data rate, and in LTE-Advanced, e.g., UE category 8, can support data rates up to 3 Gigabits per second (Gbps).
A receiver, before being able to receive downlink data from a serving radio network node, has to perform channel estimation. The channel estimation is based on a reference signal emitted by the radio network node. A number of reference signals have been defined in the LTE downlink, e.g., Cell-specific Reference Signal (CRS). CRS is transmitted in all subframes and in all resource blocks of the carrier.
The quality of the channel estimates is utterly important to support very high data rates, in particular in highly frequency- and time-selective channel (or doubly-selective channel) conditions. Cell-specific Reference Signals aided Channel Estimation (CRS-CE) unfortunately renders error floors for high data rate scenarios in severe channel conditions, e.g., Extended Typical Urban 300 (ETU300), even though advanced (iterative) MIMO detectors are employed.
Joint Channel Estimation and Data Decoding (JCED) technique is considered as one of the potential candidates to meet high data rates in doubly-selective channel conditions. Unfortunately, JCED is very complex. Therefore, we choose an iterative approach to implement it with lower complexity. Here we name this iterative approach as Joint Iterative Channel Estimation and Data Decoding (JoICED).
Within JoICED framework, the messages are exchanged among mainly three receiver components, namely, channel estimator, data detector and channel decoder. Though JoICED is considered with a tractable complexity, the implementation complexity is evidently very high due to huge matrix inversion online. Further, non-adaptive JoICED performs very poorly when the soft-data feedback quality is low, such as when the estimated Mutual Information (MI) between the transmitted bits and the soft-data feedback is low.
There are numerous approaches for the soft-Iterative Channel Estiamtion (ICE) which exist in the literature, but the current state-of-the-art may be broadly classified into two approaches.
The first approach comprises Joint Linear Minimum Mean Square Error (joint-LMMSE), also known as Maximum A-Posteriori (MAP) based soft-ICE. This approach may be optimal in the LMMSE sense; based on the assumption that the feedback soft-data is ideal. However, the complexity is formidable and realistically infeasible to implement on the real target for an UE receiver with limited capacity, due to online matrix inversion of considerable size.
The second approach is based on, e.g., Expectation and Maximization (EM), Space Alternating Generalized Expectation and maximization (SAGE) framework based soft-ICE, Variational Bayesian based soft-ICE, etc. The EM/SAGE framework based soft-ICE is having relatively lower complexity compared to the joint-LMMSE. However, most of these methods are not really designed for inherent hybrid automatic repeat request (HARQ) based LTE systems.
As mentioned previously, the joint-LMMSE based approach is optimal in the LMMSE sense but infeasible for the UE receiver implementation due to online matrix inversion of significant size, which may involve too capacity-intense calculations for the limited capability of the UE receiver.
Furthermore, the afore-referenced methods render very poor performance when the quality of the feedback soft-data (e.g., in an MI sense which is measured between the transmitted bits and the corresponding soft bits at the receiver side) is very low and thereby have serious detrimental repercussions. Moreover, the EM/SAGE-MAP framework with low-rank approximation described in the above mentioned prior-art literature is evidently not suitable for higher order modulation when the quality of the feedback soft-data is poor.
There has been suggested some techniques in the prior-art literature for soft-ICE to cope with the low-quality of the feedback soft-data. Unfortunately, these methods are not that straightforward for LTE, MIMO-OFDM based systems, which inherently have HARQ. On the other hand, one of the methods, so-called least-squares, has been proposed to cope with the low-quality of the feedback soft-data. Although this method may be employed in UE receivers, unfortunately, it does not perform very well.
Further, most importantly, scheduling, comprising enabling and/or disabling soft-ICE within JoICED framework is utterly important depending on the channel conditions in order to minimize the Bit-Error-Rate (BER), or equivalently Block-Error-Rate (BLER). Creation of a scheduling codebook has been proposed for doubly iterative receiver (i.e., only soft MIMO detector and channel decoder without soft-ICE) based on ant colony optimisation method.
Further, an Extrinsic Information Transfer chart (EXIT) function based activation scheduling has been proposed via a trellis search and thereby creating a codebook. However, EXIT function based activation scheduling has been shown to be inaccurate.
Hence, unfortunately, these known prior-art methods are very cumbersome to employ in a real UE receiver and not straightforward for implementation in a HARQ based system, since scheduling strongly depends on the channel conditions and thus may render poor performance under unfavourable signal propagation conditions.